JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jera.v9i4.11433ArticleApplication of Drone Remote Sensing Technology in Agricultural Pest Monitoring and Its Challengeshttps://artdesignp.com/journal/JERA/9/4/10.26689/jera.v9i4.11433GaoYimin,XiWujun2025942025-08-08With the increasing global population and mounting pressures on agricultural production, precise pest monitoring has become a critical factor in ensuring food security. Traditional monitoring methods, often inefficient, struggle to meet the demands of modern agriculture. Drone remote sensing technology, leveraging its high efficiency and flexibility, demonstrates significant potential in pest monitoring. Equipped with multispectral, hyperspectral, and thermal infrared sensors, drones can rapidly cover large agricultural fields, capturing high-resolution imagery and data to detect spectral variations in crops. This enables effective differentiation between healthy and infested plants, facilitating early pest identification and targeted control. This paper systematically reviews the current applications of drone remote sensing technology in pest monitoring by examining different sensor types and their use in monitoring major crop pests and diseases. It also discusses existing challenges, aiming to provide insights and references for future research.

Xu Y, 2012, Comprehensively Promoting Specialized Unified Prevention Against Crop Diseases/Pests Ensuring Food Production Safety. Agricultural Development & Equipment (in Chinese), (06): 249–250.

Zhang J, Yuan L, Wang J, et al., 2012, Remote Sensing Monitoring Research Progress on Crop Diseases/Pests. Transactions CSAM (in Chinese), (20): 01–11.

Li X, 2024, Application Research on UAV Remote Sensing Technology for Agricultural Disease/Pest Monitoring. Hebei Agricultural Machinery (in Chinese), (02): 42–44.

Li H, Chen L, Liu Y, et al., 2019, Multispectral Imaging Early Detection Plant Diseases. Precision Agriculture (in English), (03): 456–470.

Zhao X, Zhang J, Zhang D, et al., 2019, Effect Comparison Study Visible-Light vs Multispectra Sensors Evaluating Rice Sheath Blight Severity via Low-altitude Remote Sensing Platforms. Spectroscopy Spectral Analysis (in Chinese), (04): 1192–1198.

Jia X, 2023, Remote Sensing Monitoring Maize Northern Leaf Blight via UAV Multispectra Thermal Infrared Imaging, dissertation, Henan Agricultural University.

Yang G, Feng X, He Y, et al., 2022, UAV Remote Sensing Methods Latest Advances Crop Disease/Pest Stress Monitoring. Smart Agriculture (in Chinese English), (01): 01–16.

Yuan L, Zhang J, Zhao J, et al., 2013, Discrimination Severity Inversion Modeling Between Wheat Powdery Mildew Stripe Rust via Foliar Spectral Analysis. Spectroscopy Spectral Analysis (in Chinese), (06): 1608–1614.

Wu X, 2023, Application Research Hyperspectra Data Classifying Masson Pine Disease/Pest Severity. Agricultural Science Technology: Theory Edition (in Chinese), (04): 104–106.

Gu Z, Wang B, Su W, et al., 2020, Hyperspectra UAV Remote Sensing Image Recognition Technology Applied Tea Plantation Disease/Pest Control. Economist (in Chinese), (12): 61–62.

Jing H, Yang X, Yu X, et al., 2022, UAV-based Orchard Disease/Pest Monitoring System Development Hilly Areas. Southern Agricultural Machinery (in Chinese), (10): 73–75.

Oerke E, Frohling P, Steiner U, 2011, Thermographic Assessment Scab Disease Apple Leaves. Precision Agriculture (in English), (05): 699–715.

Belin E, Rousseau D, Boureau T, et al., 2013, Thermography vs Chlorophyll Fluorescence Imaging Detection Quantification Apple Scab. Computers Electronics Agriculture (in English), (90): 159–163.

Schmitz A, Kiewnick S, Schlang J, et al., 2004, High-resolution Digital Thermography Detecting Heterodera schachtii Infestation Sugar Beets. Communications Agricultural Applied Biological Sciences (in English), (03): 359–363.

Liu L, Wang J, Song X, et al., 2005, Spectral Characteristics Remote Sensing Monitoring Wheat Lodging. Journal Remote Sensing (in Chinese), (03): 323–327.

Liu T, Li Z, Feng S, et al., 2023, Hyperspectra Rice Blast Grading Detection Based LMPSO-SVM Algorithm. Transactions CSAM (in Chinese), (11): 208–216 + 235.

Kang L, 2021, Rice Blast Detection Methods Based Hyperspectra Imaging Technology, dissertation, Northeast Agricultural University.

Weng H, Yao Y, Huang D, et al., 2024, Rapid Evaluation Rice Neck Blast Resistance Combining UAV Low-altitude Remote Sensing YOLOv7 Algorithm. Transactions CSAE (in Chinese), (21): 110–118.

Liu J, Cheng J, He C, et al., 2024, Application Insecticides Key Technologies Controlling Major Rice Pests Research Progress. Modern Agrochemicals (in Chinese), (04): 07–12.

Yu R, Wenjiang H, Huichun Y, et al., 2021, Quantitative Identification of Yellow Rust in Winter Wheat with a New Spectral Index: Development and Validation Using Simulated and Experimental Data. International Journal of Applied Earth Observations and Geoinformation, 2021: 102.

Feng Z, Song L, Zhang S, et al., 2022, Wheat Powdery Mildew Monitoring Based UAV Multispectra Thermal Infrared Image Fusion. Scientia Agricultura Sinica (in Chinese), (05): 890–906.

Guo A, 2021, Wheat Stripe Rust Monitoring Methods Based Imaging Remote Sensing, dissertation, University CAS (Aerospace Information Research Institute CAS).

Zhang Z, 2024, Application Plant Protection UAVs Wheat Disease/Pest Control Xinjiang Changji City. Agricultural Engineering Technology (in Chinese), (32): 47–48.

Bai Y, Nie C, Yu X, et al., 2024, Comprehensive Analysis Hyperspectra Features Monitoring Canopy Maize Leaf Spot Disease. Computers Electronics Agriculture (in English), 225: 109350–109350.

Tian Y, Xing X, Wang X, et al., 2015, Optimal Band Selection Non-destructive Detection Corn Borers Based Hyperspectra Imaging. Journal Shenyang Agricultural University (in Chinese), (06):719–724.

Xing X, 2016, Corn Borer Detection Methods Based Hyperspectra Imaging Technology, dissertation, Shenyang Agricultural University.

Nationwide Severe Occurrence Maize Major Diseases/Pests Yearly Report, 2016, Agrochemicals (in Chinese), (02): 114.

Chen J, 2013, Application Multi-source Image Fusion Technology Cotton Disease/Pest Diagnosis. Hubei Agricultural Sciences (in Chinese), (11): 2555–2557.

Hoseny MME, Dahi HF, Shafei AME, et al., 2022, Spectroradiometer Thermal Imaging Tools Remote Sensing Early Detection Spiny Bollworm (Earias insulana Boisd.) Infestation. International Journal Tropical Insect Science (in English), (01–12).

Chen B, 2007, Hyperspectra Characteristics Spectral Diagnosis Models Cotton Verticillium Wilt, dissertation, Shihezi University.

Chen Q, 2021, Estimation Models Cotton Verticillium Wilt Severity Based Remote Sensing dissertation, Xinjiang Agricultural University.

Ma R, Zhang N, Zhang X, et al., 2024, Cotton Verticillium Wilt Monitoring Based UAV Multispectra-Visible Multi-source Feature Fusion. Computers Electronics Agriculture (in English), (217): 108628.

Jiang Y, 2025, Green Control Technology Exploration Implementation Cotton Diseases/Pests. Agricultural Technology Innovation (in Chinese), (03): 42–44.

Lan Y, Zhu Z, Deng X, et al., 2019, Monitoring Classification Citrus Huanglongbing Plants Based UAV Hyperspectra Remote Sensing. Transactions CSAE (in Chinese), (03): 92–100.

Vanegas F, Bratanov D, Powell K, et al., 2018, Novel Methodology Improving Plant Pest Surveillance Vineyards Crops Using UAV-based Hyperspectra Spatial Data. Sensors (in English), (01): 260.

Li B, Chai A, Shi Y, et al, 2025, Machine Vision-based Real-time Vegetable Leaf Disease Diagnosis Method System: CN201010237657.I (CN102334422A).

Li W, 2018, Crop Remote Sensing Identification Disease/Pest Monitoring Methods, dissertation, China Agricultural Science Technology Press.

Zhang J, Lian Y, Yang Z, et al., 2020, Plant Protection UAVs Development History Advantage-Disadvantage Analysis Application Prospects. Modern Agriculture (in Chinese), (04): 04–07.

Tian T, Wang D, Zeng Y, et al., 2020, UAV Remote Sensing-based Fine Crop Classification Research Progress. China Agricultural Information (in Chinese), (02): 01–12.